Uploaded byAkanksha Jain
Predictive Model for Loan Approval Process using SAS 9.3_M1
The document outlines a predictive model developed using a historical credit dataset to improve loan approval decisions, noting that approximately 11% of loans currently granted default. It details the methodology, including data quality checks, variable selection using different methods, and the final model's performance metrics, revealing that a significant percentage of defaulters can be classified. Additionally, it provides characteristics to avoid targeting in loan approvals, based on the model's estimates.
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Predictive Model for Loan Approval Process using SAS 9.3_M1
- 1. German Bank Loan Approval Decision Model Mohamed Ibrahim Rahul Goel Akanksha Jain Chhavi Sharma
- 2. "Houston, We've Had a Problem" On average, one of every nine loans you grant is defaulted on 2
- 3. Objec<ve & Background Objec<ve: Use historical credit dataset to develop a predicAve model to make beBer loan approval decisions in the future Background: On an average, ~11% loans granted are defaulted in the current scenario. Scope: German Credit Dataset – 1000 entries & 20 variables – Historical pre-‐loan data – Dependent variable: Good_Bad (Binary) Steps: – Data Quality Check – Variable exploraAon and transformaAon – Method SelecAon(forward, backward, stepwise) – Removal of outliers – Run the LogisAc Regression Model 3
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- 6. Data Variables Data Drill Down 6
- 7. Variable Selec<on 1 2 3 4 Full Model using STEPWISE variable selec<on method Full Model using BACKWARD variable selec<on method Full model using FORWARD variable selec<on method: Model all variables from STEPWISE + variable age_new Variables: checking_new duraAon_new history_new purpose_new savings_new installp marital coapp other foreign_new Variables: checking_new duraAon_new history_new purpose_new savings_new installp marital coapp other foreign_new Variables: checking_new duraAon_new history_new purpose_new savings_new installp marital coapp other foreign_new amount_new age_new Percent Concordant 82.3 c 0.823 Hosmer – Lemeshow Test : 0.9398 SL Entry: 0.1 Variables: checking_new duraAon_new history_new purpose_new savings_new installp marital coapp age_new other foreign_new age_new Percent Concordant 82.0 c 0.820 Hosmer – Lemeshow Test : 0.7475 Variables: checking_new duraAon_new history_new purpose_new savings_new installp marital coapp age_new other foreign_new age_new Percent Concordant 84.1 c 0.841 Hosmer – Lemeshow Test : 0.6367 SL Entry: 0.1 SL Stay: 0.05 SL Entry: 0.1 SL Stay: 0.05 Percent Concordant 81.8 c 0.818 Hosmer – Lemeshow Test : 0.7535 SL Entry: 0.1 SL Stay: 0.05 Percent Concordant 81.8 c 0.818 Hosmer – Lemeshow Test : 0.7535 SL Stay: 0.05 5 Stepwise with age_new aLer removing outliers 7
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- 9. 42.3% defaulters were classified as “bad” at 55% or more 9
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- 12. Who “not” to target The odds raAo & parameter esAmates suggest that the business should avoid targeAng individuals with the following characterisAcs (not arranged in order of priority) – – – – – – – Have lower balances in checking & savings account Have a delinquent credit history Do not have a guarantor Have other installment plans A non resident Lower age High debt-‐to-‐income raAo ** The list of characterisAcs suggested by the model is for a given set of 1000 data points and should not be extrapolated to other scenarios. This interpretaAon is only for the purpose of a classroom project and should not be used otherwise. 12
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- 14. Appendix • • • • CorrelaAon Results Proc Means & VIF Test Outliers Proc Rank 14
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- 16. Means & VIF Variance Infla<on Factor Proc Means 16
- 17. List of 16 Outliers in the Dataset 17
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